Computational Physics

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Welcome to the Computational Physics course page. This page contains resources, materials, and information organized into three main sections that you can navigate using the sidebar.

Section 1: Course Overview

Course Description

This section provides an overview of the Computational Physics course, including objectives, prerequisites, and learning outcomes.

Course Objectives:

  • Understand fundamental computational methods in physics
  • Develop programming skills for scientific computing
  • Apply numerical techniques to solve physical problems
  • Learn to analyze and visualize scientific data

Prerequisites:

  • Basic physics knowledge (mechanics, electromagnetism)
  • Programming fundamentals (preferably Python)
  • Mathematical background (calculus, linear algebra)

Schedule and Logistics

Meeting Times: [Add your schedule here]

Office Hours: [Add your office hours here]

Grading:

  • Homework assignments: 40%
  • Midterm project: 30%
  • Final project: 30%

Section 2: Course Materials

Lecture Notes

Here you’ll find lecture notes, slides, and supplementary materials for each topic covered in the course.

Topics Covered:

  1. Introduction to Scientific Computing
  2. Numerical Methods for Differential Equations
  3. Monte Carlo Methods
  4. Molecular Dynamics
  5. Statistical Mechanics Simulations
  6. Quantum Mechanics Applications

Textbooks and References

Recommended Textbooks:

  • Computational Physics by Nicholas Giordano and Hisao Nakanishi
  • Numerical Recipes by Press, Teukolsky, Vetterling, and Flannery
  • A Survey of Computational Physics by Landau, Páez, and Bordeianu

Online Resources:

  • Course repository: [Add GitHub link]
  • Python documentation
  • Scientific computing libraries (NumPy, SciPy, Matplotlib)

Code Examples

Sample code and Jupyter notebooks demonstrating key concepts:

# Example: Simple numerical integration
import numpy as np

def integrate_trapezoid(f, a, b, n=1000):
    """
    Numerical integration using the trapezoid rule
    """
    x = np.linspace(a, b, n)
    y = f(x)
    h = (b - a) / (n - 1)
    return h * (np.sum(y) - (y[0] + y[-1]) / 2)

# Test with a simple function
f = lambda x: x**2
result = integrate_trapezoid(f, 0, 1)
print(f"Integral result: {result}")

Section 3: Assignments and Projects

Homework Assignments

Regular assignments to practice computational techniques and reinforce concepts learned in class.

Assignment Guidelines:

  • Submit code and a brief report explaining your approach
  • Include plots and visualizations where appropriate
  • Comment your code clearly
  • Cite any external resources used

Current Assignments:

  1. Assignment 1: Numerical Differentiation and Integration
  2. Assignment 2: Solving ODEs - Projectile Motion
  3. Assignment 3: Random Numbers and Monte Carlo Methods
  4. Assignment 4: Molecular Dynamics Simulation

Projects

Midterm Project

Topic: [Add project description]

Deliverables:

  • Working code implementation
  • Written report (3-5 pages)
  • Brief presentation

Due Date: [Add date]

Final Project

Topic: [Add project description]

Requirements:

  • Original implementation of a computational physics problem
  • Comprehensive analysis and results
  • Final presentation to the class
  • Complete documentation

Due Date: [Add date]

Submission Instructions

All assignments should be submitted via [specify platform - e.g., GitHub, Canvas, etc.]. Include:

  • Source code files
  • README with instructions to run your code
  • Report in PDF format
  • Any additional data files needed

Contact and Support

For questions or additional support, please:

  • Attend office hours
  • Post on the course forum/discussion board
  • Email: [your email]

Important: Please include “[Comp Phys]” in the subject line of any emails related to this course.